Source code for colour.colorimetry.transformations

"""
Colour Matching Functions Transformations
=========================================

Defines various educational objects for colour matching functions
transformations:

-   :func:`colour.colorimetry.RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs`
-   :func:`colour.colorimetry.RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs`
-   :func:`colour.colorimetry.RGB_10_degree_cmfs_to_LMS_10_degree_cmfs`
-   :func:`colour.colorimetry.LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs`
-   :func:`colour.colorimetry.LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs`

References
----------
-   :cite:`CIETC1-362006a` : CIE TC 1-36. (2006). CIE 170-1:2006 Fundamental
    Chromaticity Diagram with Physiological Axes - Part 1. Commission
    Internationale de l'Eclairage. ISBN:978-3-901906-46-6
-   :cite:`CVRLp` : CVRL. (n.d.). CIE (2012) 10-deg XYZ
    "physiologically-relevant" colour matching functions. Retrieved June 25,
    2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz10.htm
-   :cite:`CVRLv` : CVRL. (n.d.). CIE (2012) 2-deg XYZ
    "physiologically-relevant" colour matching functions. Retrieved June 25,
    2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz2.htm
-   :cite:`Wyszecki2000be` : Wyszecki, Gùˆnther, & Stiles, W. S. (2000). The
    CIE 1964 Standard Observer. In Color Science: Concepts and Methods,
    Quantitative Data and Formulae (p. 141). Wiley. ISBN:978-0-471-39918-6
-   :cite:`Wyszecki2000bg` : Wyszecki, Gùˆnther, & Stiles, W. S. (2000). Table
    1(3.3.3). In Color Science: Concepts and Methods, Quantitative Data and
    Formulae (pp. 138-139). Wiley. ISBN:978-0-471-39918-6
"""

from __future__ import annotations

import numpy as np

from colour.algebra import vector_dot
from colour.colorimetry import (
    MSDS_CMFS_LMS,
    MSDS_CMFS_RGB,
    SDS_LEFS_PHOTOPIC,
    reshape_sd,
)
from colour.hints import FloatingOrArrayLike, NDArray
from colour.utilities import tstack

__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "New BSD License - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"

__all__ = [
    "RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs",
    "RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs",
    "RGB_10_degree_cmfs_to_LMS_10_degree_cmfs",
    "LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs",
    "LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs",
]


[docs]def RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs( wavelength: FloatingOrArrayLike, ) -> NDArray: """ Convert *Wright & Guild 1931 2 Degree RGB CMFs* colour matching functions into the *CIE 1931 2 Degree Standard Observer* colour matching functions. Parameters ---------- wavelength Wavelength :math:`\\lambda` in nm. Returns ------- :class:`numpy.ndarray` *CIE 1931 2 Degree Standard Observer* spectral tristimulus values. Notes ----- - Data for the *CIE 1931 2 Degree Standard Observer* already exists, this definition is intended for educational purpose. References ---------- :cite:`Wyszecki2000bg` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0113577..., 0.004102 , 0. ]) """ cmfs = MSDS_CMFS_RGB["Wright & Guild 1931 2 Degree RGB CMFs"] rgb_bar = cmfs[wavelength] rgb = rgb_bar / np.sum(rgb_bar) M1 = np.array( [ [0.49000, 0.31000, 0.20000], [0.17697, 0.81240, 0.01063], [0.00000, 0.01000, 0.99000], ] ) M2 = np.array( [ [0.66697, 1.13240, 1.20063], [0.66697, 1.13240, 1.20063], [0.66697, 1.13240, 1.20063], ] ) xyz = vector_dot(M1, rgb) / vector_dot(M2, rgb) x, y, z = xyz[..., 0], xyz[..., 1], xyz[..., 2] V = reshape_sd( SDS_LEFS_PHOTOPIC["CIE 1924 Photopic Standard Observer"], cmfs.shape ) L = V[wavelength] x_bar = x / y * L y_bar = L z_bar = z / y * L xyz_bar = tstack([x_bar, y_bar, z_bar]) return xyz_bar
[docs]def RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs( wavelength: FloatingOrArrayLike, ) -> NDArray: """ Convert *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching functions into the *CIE 1964 10 Degree Standard Observer* colour matching functions. Parameters ---------- wavelength Wavelength :math:`\\lambda` in nm. Returns ------- :class:`numpy.ndarray` *CIE 1964 10 Degree Standard Observer* spectral tristimulus values. Notes ----- - Data for the *CIE 1964 10 Degree Standard Observer* already exists, this definition is intended for educational purpose. References ---------- :cite:`Wyszecki2000be` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0096432..., 0.0037526..., -0.0000041...]) """ cmfs = MSDS_CMFS_RGB["Stiles & Burch 1959 10 Degree RGB CMFs"] rgb_bar = cmfs[wavelength] M = np.array( [ [0.341080, 0.189145, 0.387529], [0.139058, 0.837460, 0.073316], [0.000000, 0.039553, 2.026200], ] ) xyz_bar = vector_dot(M, rgb_bar) return xyz_bar
[docs]def RGB_10_degree_cmfs_to_LMS_10_degree_cmfs( wavelength: FloatingOrArrayLike, ) -> NDArray: """ Convert *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching functions into the *Stockman & Sharpe 10 Degree Cone Fundamentals* spectral sensitivity functions. Parameters ---------- wavelength Wavelength :math:`\\lambda` in nm. Returns ------- :class:`numpy.ndarray` *Stockman & Sharpe 10 Degree Cone Fundamentals* spectral tristimulus values. Notes ----- - Data for the *Stockman & Sharpe 10 Degree Cone Fundamentals* already exists, this definition is intended for educational purpose. References ---------- :cite:`CIETC1-362006a` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0052860..., 0.0003252..., 0. ]) """ cmfs = MSDS_CMFS_RGB["Stiles & Burch 1959 10 Degree RGB CMFs"] rgb_bar = cmfs[wavelength] M = np.array( [ [0.1923252690, 0.749548882, 0.0675726702], [0.0192290085, 0.940908496, 0.113830196], [0.0000000000, 0.0105107859, 0.991427669], ] ) lms_bar = vector_dot(M, rgb_bar) lms_bar[..., -1][np.asarray(np.asarray(wavelength) > 505)] = 0 return lms_bar
[docs]def LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs( wavelength: FloatingOrArrayLike, ) -> NDArray: """ Convert *Stockman & Sharpe 2 Degree Cone Fundamentals* colour matching functions into the *CIE 2015 2 Degree Standard Observer* colour matching functions. Parameters ---------- wavelength Wavelength :math:`\\lambda` in nm. Returns ------- :class:`numpy.ndarray` *CIE 2015 2 Degree Standard Observer* spectral tristimulus values. Notes ----- - Data for the *CIE 2015 2 Degree Standard Observer* already exists, this definition is intended for educational purpose. References ---------- :cite:`CVRLv` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0109677..., 0.0041959..., 0. ]) """ cmfs = MSDS_CMFS_LMS["Stockman & Sharpe 2 Degree Cone Fundamentals"] lms_bar = cmfs[wavelength] M = np.array( [ [1.94735469, -1.41445123, 0.36476327], [0.68990272, 0.34832189, 0.00000000], [0.00000000, 0.00000000, 1.93485343], ] ) xyz_bar = vector_dot(M, lms_bar) return xyz_bar
[docs]def LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs( wavelength: FloatingOrArrayLike, ) -> NDArray: """ Convert *Stockman & Sharpe 10 Degree Cone Fundamentals* colour matching functions into the *CIE 2015 10 Degree Standard Observer* colour matching functions. Parameters ---------- wavelength Wavelength :math:`\\lambda` in nm. Returns ------- :class:`numpy.ndarray` *CIE 2015 10 Degree Standard Observer* spectral tristimulus values. Notes ----- - Data for the *CIE 2015 10 Degree Standard Observer* already exists, this definition is intended for educational purpose. References ---------- :cite:`CVRLp` Examples -------- >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True): ... LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS array([ 0.0098162..., 0.0037761..., 0. ]) """ cmfs = MSDS_CMFS_LMS["Stockman & Sharpe 10 Degree Cone Fundamentals"] lms_bar = cmfs[wavelength] M = np.array( [ [1.93986443, -1.34664359, 0.43044935], [0.69283932, 0.34967567, 0.00000000], [0.00000000, 0.00000000, 2.14687945], ] ) xyz_bar = vector_dot(M, lms_bar) return xyz_bar